Kaggle Tabular Playground Series - May 2021

• Objective:
• Exploratory Data Analysis
• Naive Baseline Model - Random Forest
  • SMOTE:
• CatBoost
• Appendix
  • Principal Component Analysis
  • UMAP (Universal Manifold Approximation and Projection)
  • - Data is uniformly distributed on Reimannian manifold
    • Oversampling
    • Undersampling
    • Bagging Classifier
    • Combination of Over and Undersampling
    • Baseline model - SVM
    • Using the top important features only
    • Generate the submission

Objective:

I wanted to get into the habit of seriously taking part in some Kaggle competetions. The Tabluar Playground Series seemed like a good place to start. The competetion for May 2021 involves a multi-class classification problem for a consumer products dataset. The dataset was a bit skewed and quite sparse with many features. I tried a few models and learnt quite a bit from the notebooks and the discussion forums. This post is basically a walkthrough of my approach to tackle this problem.

import pandas as pd
train = pd.read_csv('train.csv.zip')
test = pd.read_csv('test.csv.zip') 
sample_submission = pd.read_csv('sample_submission.csv.zip')

train[45:50]

	id	feature_0	feature_1	feature_2	feature_3	feature_4	feature_5	feature_6	feature_7	feature_8	feature_9	feature_10	feature_11	feature_12	feature_13	feature_14	feature_15	feature_16	feature_17	feature_18	feature_19	feature_20	feature_21	feature_22	feature_23	feature_24	feature_25	feature_26	feature_27	feature_28	feature_29	feature_30	feature_31	feature_32	feature_33	feature_34	feature_35	feature_36	feature_37	feature_38	feature_39	feature_40	feature_41	feature_42	feature_43	feature_44	feature_45	feature_46	feature_47	feature_48	feature_49	target
45	45	0	0	0	0	0	0	1	0	9	0	0	0	0	0	7	1	0	0	0	33	0	0	0	0	1	1	1	0	2	0	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	1	Class_2
46	46	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	1	5	0	9	0	1	0	0	0	0	0	0	0	0	0	8	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	1	0	Class_2
47	47	0	0	0	0	0	0	9	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	2	0	0	0	0	0	0	0	0	1	3	0	0	0	0	0	3	1	0	0	0	3	0	0	0	Class_1
48	48	0	0	0	12	0	0	1	0	0	0	0	3	2	0	2	0	0	0	0	0	0	0	0	1	0	3	0	1	0	0	0	0	0	0	0	0	0	1	9	0	0	0	0	0	0	0	0	0	0	1	Class_3
49	49	0	0	0	0	0	0	2	4	1	2	0	0	0	0	4	0	0	1	0	0	0	0	0	3	0	1	0	0	1	0	0	0	0	0	0	0	0	0	2	0	3	0	0	0	0	1	0	0	0	1	Class_2

Exploratory Data Analysis

Lets do some EDA to get a sense of the data

train.describe()

	id	feature_0	feature_1	feature_2	feature_3	feature_4	feature_5	feature_6	feature_7	feature_8	feature_9	feature_10	feature_11	feature_12	feature_13	feature_14	feature_15	feature_16	feature_17	feature_18	feature_19	feature_20	feature_21	feature_22	feature_23	feature_24	feature_25	feature_26	feature_27	feature_28	feature_29	feature_30	feature_31	feature_32	feature_33	feature_34	feature_35	feature_36	feature_37	feature_38	feature_39	feature_40	feature_41	feature_42	feature_43	feature_44	feature_45	feature_46	feature_47	feature_48	feature_49	target2
count	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.00000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.00000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.00000	100000.000000	100000.00000	100000.000000
mean	49999.500000	0.257830	0.431720	0.114130	0.590550	0.599770	0.160820	0.731490	1.228920	0.903350	0.940120	0.313680	0.403750	0.461470	0.07565	2.597320	1.242030	0.364830	0.740000	0.471220	1.564780	0.242890	0.661970	0.184740	0.388040	1.04013	0.408630	0.353550	0.300680	1.106060	0.169600	0.294500	1.372040	0.277510	0.612460	1.122150	0.908980	0.116530	0.398310	2.997170	0.674640	0.712270	0.582070	0.529230	0.616310	0.135100	0.358660	0.516810	0.39004	0.970850	0.55712	1.381160
std	28867.657797	0.929033	1.977862	0.519584	1.844558	2.785531	0.601149	2.343465	2.692732	3.415258	1.904172	1.121168	1.011418	1.128729	0.35901	4.655092	2.160111	1.284152	1.819388	1.096423	4.749449	0.927461	2.182703	0.553854	1.215796	2.53991	1.321669	1.491937	1.592092	1.879629	0.739582	1.352963	3.319325	1.460705	2.283595	2.493045	2.952305	0.411014	1.095268	5.386551	3.732428	1.721863	2.003114	2.300826	2.360955	0.627592	1.464187	2.171415	1.48735	2.576615	1.68093	0.810891
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	-2.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	-1.000000	-1.000000	-2.000000	0.000000	0.000000	-2.000000	0.000000	0.000000	-8.000000	-5.000000	0.000000	0.000000	-2.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.00000	0.000000
25%	24999.750000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.00000	1.000000
50%	49999.500000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.00000	1.000000
75%	74999.250000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.00000	3.000000	2.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	1.00000	0.000000	0.000000	0.000000	2.000000	0.000000	0.000000	1.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	4.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	1.000000	0.00000	2.000000
max	99999.000000	10.000000	31.000000	6.000000	26.000000	38.000000	10.000000	27.000000	31.000000	39.000000	17.000000	16.000000	12.000000	11.000000	3.00000	51.000000	20.000000	19.000000	16.000000	13.000000	55.000000	17.000000	36.000000	4.000000	19.000000	34.00000	23.000000	21.000000	31.000000	23.000000	13.000000	41.000000	46.000000	27.000000	24.000000	25.000000	43.000000	3.000000	14.000000	66.000000	66.000000	21.000000	32.000000	37.000000	33.000000	9.000000	26.000000	29.000000	25.00000	44.000000	20.00000	3.000000

It appears that the data is very sparse and fortunately contains no null values. We will need to do some dimensiontionality reduction (PCA) and pick the best features to reduce the noise in the input dataset.

train['target'].hist()

<matplotlib.axes._subplots.AxesSubplot at 0x7fc5009f2c90>

Looks like Class_2 has a lot more data points than the other 3 classes combined. This is a pretty skewed datset and will bias the classifier, I am thinking of using SMOTE to handle the issue of class imbalance in the dataset.

(train == 0).sum().sum()/(train.shape[0]*train.shape[1])*100

76.35163461538461

Another thing I noticed was that 75 % of the data values are 0s, so the dataset is very sparse. Lets get started and build a small simple model. Firstly we will clean up the datasets and create the variables necessary to start on building the model.

train.target = pd.Categorical(train.target)
train['target2'] = train.target.cat.codes

from sklearn.model_selection import train_test_split
indep_vars = ['feature_' + str(i) for i in range(50)]
dep_var = ['target2']

X = train[indep_vars].to_numpy()
y = train[dep_var].to_numpy().ravel()


X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.3, random_state=42)

X_test = test[indep_vars].to_numpy()

Naive Baseline Model - Random Forest

Here we are building a simple model using a RandomForest classifier. The main purpose of this is to get a baseline perfromance and also identify key features that can be used for later analyses.

X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.3, random_state=42)

from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(n_jobs=-1, n_estimators=40, max_samples=70000,
       max_features=0.5, min_samples_leaf=5, oob_score=True)

rf.fit(X_train, y_train)
rf.score(X_valid, y_valid)

0.5757

from sklearn.metrics import plot_confusion_matrix
plot_confusion_matrix(rf, X_valid, y_valid)

<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fc4ea6d1e50>

rf.predict_proba(X_test)

array([[0.14013549, 0.5686321 , 0.14980887, 0.14142354],
       [0.0913934 , 0.68614493, 0.16746077, 0.0550009 ],
       [0.05243985, 0.61068121, 0.20493451, 0.13194442],
       ...,
       [0.09103053, 0.54871624, 0.20957293, 0.1506803 ],
       [0.04416667, 0.65122176, 0.15509768, 0.14951389],
       [0.13996562, 0.51813905, 0.23093735, 0.11095798]])

As expected the model does not perform very well and most of the classes are mis-classified in favour of the majority class here. Let's see how well this did on Kaggle

SMOTE:

To deal with the class imbalance issue, I decide to try out SMOTE and see if the helps in the prediction.

from imblearn.over_sampling import  KMeansSMOTE
ros =  KMeansSMOTE(random_state=0)
X_resampled, y_resampled = ros.fit_resample(X, y)

X_train, X_valid, y_train, y_valid = train_test_split(X_resampled, y_resampled, test_size=0.3, random_state=42)

rf_SMOTE = RandomForestClassifier(n_jobs=-1, n_estimators=40, max_samples=70000,
       max_features=0.5, min_samples_leaf=5, oob_score=True)

rf_SMOTE.fit(X_train, y_train)
print(rf_SMOTE.score(X_train, y_train))
rf_SMOTE.score(X_valid, y_valid)

from sklearn.metrics import plot_confusion_matrix
plot_confusion_matrix(rf_SMOTE, X_valid, y_valid)

submission2 = submissionFile(rf_SMOTE, generate_csv=True)

!kaggle competitions submit -c tabular-playground-series-may-2021 -f submission.csv -m "SMOTE Oversampling - Randomforests"

Unfortunately SMOTE did not help improve the quality of the predictions.

CatBoost

After trying PCA and SMOTE after tweaking the parameters and getting worse predictions than the simpleRandom Forests mosel. I came across the CatBoost Calssifier from one of the notebooks on Kaggle.

from catboost import Pool, CatBoostClassifier
from sklearn.model_selection import StratifiedKFold
def train_models(df, columns, cat_features, random_state, n_splits):

    models = []
    folds = StratifiedKFold(n_splits=n_splits, shuffle=True,
                            random_state=random_state)  # create folds
    X_train = df[columns]
    y_train = df['target']
    for n_fold, (train_idx, valid_idx) in enumerate(folds.split(X_train,  y_train)):
        print('Fold {}/{}'.format(n_fold, n_splits))
        train_X, train_y = X_train.iloc[train_idx], y_train.iloc[train_idx]
        valid_X, valid_y = X_train.iloc[valid_idx], y_train.iloc[valid_idx]
        dataset = Pool(train_X, train_y, cat_features)
        evalset = Pool(valid_X, valid_y, cat_features)
        model = CatBoostClassifier(
            task_type="GPU",
            depth=4,
            max_ctr_complexity=15,
            # border_count=1024,
            iterations=50000,
            od_wait=1000, od_type='Iter',
            # l2_leaf_reg=0.01,
            learning_rate=0.01,
            min_data_in_leaf=1,
            use_best_model=True,
            loss_function='MultiClass'

        )
        model.fit(dataset, plot=False, verbose=500, eval_set=evalset)        
        _record = {
            'model': model,
        }
        models.append(_record)

    return models

models = train_models(df=train, columns=indep_vars, cat_features=indep_vars, random_state=43, n_splits=9)

Fold 0/9
0:	learn: 1.3803258	test: 1.3803090	best: 1.3803090 (0)	total: 14.2ms	remaining: 11m 52s
500:	learn: 1.1015525	test: 1.1040991	best: 1.1040991 (500)	total: 4.32s	remaining: 7m 6s
1000:	learn: 1.0941982	test: 1.0985177	best: 1.0985177 (1000)	total: 8.64s	remaining: 7m 3s
1500:	learn: 1.0898579	test: 1.0957048	best: 1.0957048 (1500)	total: 12.8s	remaining: 6m 52s
2000:	learn: 1.0868512	test: 1.0941372	best: 1.0941372 (2000)	total: 16.8s	remaining: 6m 43s
2500:	learn: 1.0844617	test: 1.0931781	best: 1.0931781 (2500)	total: 21.1s	remaining: 6m 41s
3000:	learn: 1.0825322	test: 1.0925268	best: 1.0925268 (3000)	total: 25.3s	remaining: 6m 35s
3500:	learn: 1.0808074	test: 1.0920747	best: 1.0920747 (3500)	total: 29.4s	remaining: 6m 30s
4000:	learn: 1.0792429	test: 1.0916825	best: 1.0916766 (3997)	total: 33.4s	remaining: 6m 24s
4500:	learn: 1.0778355	test: 1.0914039	best: 1.0914028 (4487)	total: 37.5s	remaining: 6m 19s
5000:	learn: 1.0764980	test: 1.0912250	best: 1.0912243 (4994)	total: 41.7s	remaining: 6m 14s
5500:	learn: 1.0752396	test: 1.0910465	best: 1.0910456 (5496)	total: 45.9s	remaining: 6m 11s
6000:	learn: 1.0740556	test: 1.0909633	best: 1.0909506 (5957)	total: 50.1s	remaining: 6m 7s
6500:	learn: 1.0729456	test: 1.0908359	best: 1.0908335 (6496)	total: 54.3s	remaining: 6m 3s
7000:	learn: 1.0718183	test: 1.0907819	best: 1.0907808 (6998)	total: 58.5s	remaining: 5m 59s
7500:	learn: 1.0707242	test: 1.0907298	best: 1.0907290 (7432)	total: 1m 2s	remaining: 5m 54s
8000:	learn: 1.0696695	test: 1.0906369	best: 1.0906315 (7980)	total: 1m 6s	remaining: 5m 51s
8500:	learn: 1.0686489	test: 1.0906368	best: 1.0906144 (8239)	total: 1m 11s	remaining: 5m 47s
9000:	learn: 1.0676562	test: 1.0906158	best: 1.0906020 (8887)	total: 1m 15s	remaining: 5m 42s
9500:	learn: 1.0666790	test: 1.0906080	best: 1.0905994 (9200)	total: 1m 19s	remaining: 5m 38s
10000:	learn: 1.0657167	test: 1.0905905	best: 1.0905806 (9982)	total: 1m 23s	remaining: 5m 34s
10500:	learn: 1.0647863	test: 1.0905826	best: 1.0905806 (9982)	total: 1m 27s	remaining: 5m 30s
11000:	learn: 1.0637850	test: 1.0906022	best: 1.0905774 (10586)	total: 1m 31s	remaining: 5m 26s
11500:	learn: 1.0628334	test: 1.0906119	best: 1.0905774 (10586)	total: 1m 36s	remaining: 5m 21s
bestTest = 1.090577402
bestIteration = 10586
Shrink model to first 10587 iterations.
Fold 1/9
0:	learn: 1.3803360	test: 1.3803279	best: 1.3803279 (0)	total: 12.3ms	remaining: 10m 15s
500:	learn: 1.1014912	test: 1.1025499	best: 1.1025499 (500)	total: 4.42s	remaining: 7m 16s
1000:	learn: 1.0939441	test: 1.0969725	best: 1.0969725 (1000)	total: 8.48s	remaining: 6m 55s
1500:	learn: 1.0895491	test: 1.0944022	best: 1.0944022 (1500)	total: 12.8s	remaining: 6m 53s
2000:	learn: 1.0864772	test: 1.0929863	best: 1.0929863 (2000)	total: 16.9s	remaining: 6m 46s
2500:	learn: 1.0841486	test: 1.0921176	best: 1.0921176 (2500)	total: 21.2s	remaining: 6m 43s
3000:	learn: 1.0821978	test: 1.0915227	best: 1.0915227 (3000)	total: 25.5s	remaining: 6m 39s
3500:	learn: 1.0805093	test: 1.0911557	best: 1.0911541 (3499)	total: 29.6s	remaining: 6m 33s
4000:	learn: 1.0790046	test: 1.0908112	best: 1.0908112 (4000)	total: 33.8s	remaining: 6m 29s
4500:	learn: 1.0776475	test: 1.0905735	best: 1.0905719 (4499)	total: 38.1s	remaining: 6m 24s
5000:	learn: 1.0763807	test: 1.0903827	best: 1.0903788 (4997)	total: 42.2s	remaining: 6m 20s
5500:	learn: 1.0751514	test: 1.0902932	best: 1.0902874 (5496)	total: 46.4s	remaining: 6m 15s
6000:	learn: 1.0739319	test: 1.0902063	best: 1.0902046 (5908)	total: 50.5s	remaining: 6m 10s
6500:	learn: 1.0727690	test: 1.0901269	best: 1.0901170 (6492)	total: 54.7s	remaining: 6m 5s
7000:	learn: 1.0716421	test: 1.0900394	best: 1.0900349 (6991)	total: 58.8s	remaining: 6m
7500:	learn: 1.0705629	test: 1.0900175	best: 1.0900028 (7477)	total: 1m 2s	remaining: 5m 56s
8000:	learn: 1.0695212	test: 1.0899541	best: 1.0899479 (7904)	total: 1m 7s	remaining: 5m 52s
8500:	learn: 1.0684655	test: 1.0899408	best: 1.0899365 (8487)	total: 1m 11s	remaining: 5m 48s
9000:	learn: 1.0674269	test: 1.0898715	best: 1.0898657 (8929)	total: 1m 15s	remaining: 5m 44s
9500:	learn: 1.0664584	test: 1.0898152	best: 1.0898050 (9463)	total: 1m 19s	remaining: 5m 39s
10000:	learn: 1.0654737	test: 1.0898123	best: 1.0897987 (9840)	total: 1m 23s	remaining: 5m 35s
10500:	learn: 1.0645035	test: 1.0897702	best: 1.0897668 (10494)	total: 1m 27s	remaining: 5m 30s
11000:	learn: 1.0635681	test: 1.0897879	best: 1.0897546 (10708)	total: 1m 32s	remaining: 5m 26s
11500:	learn: 1.0626389	test: 1.0897974	best: 1.0897546 (10708)	total: 1m 36s	remaining: 5m 22s
bestTest = 1.089754628
bestIteration = 10708
Shrink model to first 10709 iterations.
Fold 2/9
0:	learn: 1.3803308	test: 1.3803088	best: 1.3803088 (0)	total: 12.6ms	remaining: 10m 30s
500:	learn: 1.1026537	test: 1.1027110	best: 1.1027110 (500)	total: 4.32s	remaining: 7m 6s
1000:	learn: 1.0952884	test: 1.0966361	best: 1.0966361 (1000)	total: 8.42s	remaining: 6m 52s
1500:	learn: 1.0907006	test: 1.0935080	best: 1.0935080 (1500)	total: 12.7s	remaining: 6m 50s
2000:	learn: 1.0875564	test: 1.0918573	best: 1.0918573 (2000)	total: 16.8s	remaining: 6m 43s
2500:	learn: 1.0850765	test: 1.0908301	best: 1.0908301 (2500)	total: 21s	remaining: 6m 38s
3000:	learn: 1.0830559	test: 1.0901476	best: 1.0901425 (2995)	total: 25.1s	remaining: 6m 33s
3500:	learn: 1.0812809	test: 1.0896141	best: 1.0896141 (3500)	total: 29.2s	remaining: 6m 28s
4000:	learn: 1.0796644	test: 1.0892919	best: 1.0892897 (3998)	total: 33.5s	remaining: 6m 25s
4500:	learn: 1.0781884	test: 1.0889942	best: 1.0889942 (4500)	total: 37.6s	remaining: 6m 20s
5000:	learn: 1.0768332	test: 1.0887151	best: 1.0887127 (4982)	total: 41.8s	remaining: 6m 16s
5500:	learn: 1.0755192	test: 1.0885238	best: 1.0885234 (5494)	total: 46s	remaining: 6m 12s
6000:	learn: 1.0743400	test: 1.0883543	best: 1.0883502 (5967)	total: 50.3s	remaining: 6m 8s
6500:	learn: 1.0731217	test: 1.0882011	best: 1.0881995 (6497)	total: 54.4s	remaining: 6m 3s
7000:	learn: 1.0719769	test: 1.0880809	best: 1.0880809 (7000)	total: 58.5s	remaining: 5m 59s
7500:	learn: 1.0708411	test: 1.0879616	best: 1.0879544 (7467)	total: 1m 2s	remaining: 5m 55s
8000:	learn: 1.0698053	test: 1.0878881	best: 1.0878807 (7971)	total: 1m 6s	remaining: 5m 51s
8500:	learn: 1.0687541	test: 1.0877954	best: 1.0877954 (8500)	total: 1m 11s	remaining: 5m 46s
9000:	learn: 1.0677062	test: 1.0877484	best: 1.0877483 (8991)	total: 1m 15s	remaining: 5m 42s
9500:	learn: 1.0667648	test: 1.0876857	best: 1.0876828 (9344)	total: 1m 19s	remaining: 5m 38s
10000:	learn: 1.0657472	test: 1.0876434	best: 1.0876398 (9965)	total: 1m 23s	remaining: 5m 34s
10500:	learn: 1.0647776	test: 1.0875889	best: 1.0875848 (10464)	total: 1m 27s	remaining: 5m 30s
11000:	learn: 1.0637846	test: 1.0876084	best: 1.0875726 (10660)	total: 1m 32s	remaining: 5m 26s
11500:	learn: 1.0628153	test: 1.0875574	best: 1.0875568 (11495)	total: 1m 36s	remaining: 5m 22s
12000:	learn: 1.0618778	test: 1.0875947	best: 1.0875549 (11619)	total: 1m 40s	remaining: 5m 18s
12500:	learn: 1.0609167	test: 1.0875855	best: 1.0875549 (11619)	total: 1m 44s	remaining: 5m 14s
bestTest = 1.087554879
bestIteration = 11619
Shrink model to first 11620 iterations.
Fold 3/9
0:	learn: 1.3803280	test: 1.3803256	best: 1.3803256 (0)	total: 12.9ms	remaining: 10m 43s
500:	learn: 1.1017146	test: 1.1031018	best: 1.1031018 (500)	total: 4.34s	remaining: 7m 9s
1000:	learn: 1.0943061	test: 1.0973328	best: 1.0973328 (1000)	total: 8.49s	remaining: 6m 55s
1500:	learn: 1.0899892	test: 1.0945148	best: 1.0945148 (1500)	total: 12.6s	remaining: 6m 47s
2000:	learn: 1.0869874	test: 1.0929405	best: 1.0929403 (1998)	total: 16.9s	remaining: 6m 45s
2500:	learn: 1.0846467	test: 1.0918651	best: 1.0918651 (2500)	total: 21.2s	remaining: 6m 43s
3000:	learn: 1.0826715	test: 1.0911602	best: 1.0911602 (3000)	total: 25.5s	remaining: 6m 39s
3500:	learn: 1.0809048	test: 1.0905949	best: 1.0905948 (3496)	total: 29.7s	remaining: 6m 35s
4000:	learn: 1.0793847	test: 1.0902506	best: 1.0902500 (3999)	total: 34s	remaining: 6m 30s
4500:	learn: 1.0779492	test: 1.0899494	best: 1.0899494 (4500)	total: 38.2s	remaining: 6m 26s
5000:	learn: 1.0766480	test: 1.0896870	best: 1.0896827 (4998)	total: 42.6s	remaining: 6m 22s
5500:	learn: 1.0753804	test: 1.0895024	best: 1.0895023 (5496)	total: 46.7s	remaining: 6m 17s
6000:	learn: 1.0741242	test: 1.0892815	best: 1.0892815 (6000)	total: 50.9s	remaining: 6m 13s
6500:	learn: 1.0730024	test: 1.0891741	best: 1.0891738 (6498)	total: 55.3s	remaining: 6m 10s
7000:	learn: 1.0718220	test: 1.0890617	best: 1.0890613 (6996)	total: 59.6s	remaining: 6m 5s
7500:	learn: 1.0707272	test: 1.0889816	best: 1.0889797 (7494)	total: 1m 3s	remaining: 6m 1s
8000:	learn: 1.0696657	test: 1.0888606	best: 1.0888604 (7974)	total: 1m 7s	remaining: 5m 56s
8500:	learn: 1.0686267	test: 1.0887702	best: 1.0887702 (8500)	total: 1m 12s	remaining: 5m 52s
9000:	learn: 1.0675666	test: 1.0887295	best: 1.0887295 (9000)	total: 1m 16s	remaining: 5m 48s
9500:	learn: 1.0665450	test: 1.0886619	best: 1.0886575 (9450)	total: 1m 20s	remaining: 5m 43s
10000:	learn: 1.0655339	test: 1.0885993	best: 1.0885961 (9991)	total: 1m 24s	remaining: 5m 39s
10500:	learn: 1.0645720	test: 1.0885617	best: 1.0885602 (10491)	total: 1m 29s	remaining: 5m 34s
11000:	learn: 1.0636203	test: 1.0885606	best: 1.0885506 (10589)	total: 1m 33s	remaining: 5m 30s
11500:	learn: 1.0626753	test: 1.0885153	best: 1.0885110 (11494)	total: 1m 37s	remaining: 5m 26s
12000:	learn: 1.0617383	test: 1.0885190	best: 1.0885056 (11543)	total: 1m 41s	remaining: 5m 21s
12500:	learn: 1.0607700	test: 1.0885328	best: 1.0885056 (11543)	total: 1m 45s	remaining: 5m 17s
bestTest = 1.088505602
bestIteration = 11543
Shrink model to first 11544 iterations.
Fold 4/9
0:	learn: 1.3803340	test: 1.3803078	best: 1.3803078 (0)	total: 12.7ms	remaining: 10m 37s
500:	learn: 1.1019896	test: 1.1042628	best: 1.1042628 (500)	total: 4.29s	remaining: 7m 4s
1000:	learn: 1.0944736	test: 1.0987621	best: 1.0987621 (1000)	total: 8.54s	remaining: 6m 58s
1500:	learn: 1.0900459	test: 1.0962874	best: 1.0962874 (1500)	total: 12.8s	remaining: 6m 54s
2000:	learn: 1.0868669	test: 1.0948300	best: 1.0948300 (2000)	total: 17.1s	remaining: 6m 49s
2500:	learn: 1.0843865	test: 1.0939292	best: 1.0939281 (2498)	total: 21.3s	remaining: 6m 44s
3000:	learn: 1.0823799	test: 1.0933250	best: 1.0933245 (2999)	total: 25.4s	remaining: 6m 37s
3500:	learn: 1.0806365	test: 1.0929095	best: 1.0929095 (3500)	total: 29.7s	remaining: 6m 34s
4000:	learn: 1.0791316	test: 1.0925843	best: 1.0925826 (3996)	total: 34.1s	remaining: 6m 32s
4500:	learn: 1.0777102	test: 1.0923641	best: 1.0923641 (4500)	total: 38.5s	remaining: 6m 29s
5000:	learn: 1.0764284	test: 1.0921719	best: 1.0921719 (5000)	total: 42.6s	remaining: 6m 23s
5500:	learn: 1.0751858	test: 1.0920199	best: 1.0920182 (5491)	total: 46.8s	remaining: 6m 18s
6000:	learn: 1.0739876	test: 1.0919163	best: 1.0919153 (5999)	total: 51s	remaining: 6m 14s
6500:	learn: 1.0728887	test: 1.0918068	best: 1.0918068 (6500)	total: 55.4s	remaining: 6m 10s
7000:	learn: 1.0717790	test: 1.0917216	best: 1.0917216 (7000)	total: 59.7s	remaining: 6m 6s
7500:	learn: 1.0707427	test: 1.0916719	best: 1.0916677 (7495)	total: 1m 4s	remaining: 6m 3s
8000:	learn: 1.0697138	test: 1.0916294	best: 1.0916285 (7957)	total: 1m 8s	remaining: 5m 59s
8500:	learn: 1.0686656	test: 1.0915897	best: 1.0915771 (8388)	total: 1m 12s	remaining: 5m 56s
9000:	learn: 1.0676797	test: 1.0915682	best: 1.0915653 (8998)	total: 1m 17s	remaining: 5m 51s
9500:	learn: 1.0667159	test: 1.0915386	best: 1.0915321 (9486)	total: 1m 21s	remaining: 5m 47s
10000:	learn: 1.0657497	test: 1.0915615	best: 1.0915321 (9486)	total: 1m 25s	remaining: 5m 43s
10500:	learn: 1.0647959	test: 1.0915209	best: 1.0915209 (10500)	total: 1m 30s	remaining: 5m 38s
11000:	learn: 1.0638349	test: 1.0915025	best: 1.0915018 (10995)	total: 1m 34s	remaining: 5m 34s
11500:	learn: 1.0629097	test: 1.0915072	best: 1.0914951 (11389)	total: 1m 38s	remaining: 5m 29s
12000:	learn: 1.0620010	test: 1.0914735	best: 1.0914697 (11995)	total: 1m 42s	remaining: 5m 25s
12500:	learn: 1.0610779	test: 1.0914550	best: 1.0914549 (12498)	total: 1m 47s	remaining: 5m 21s
13000:	learn: 1.0601210	test: 1.0914663	best: 1.0914455 (12903)	total: 1m 51s	remaining: 5m 16s
13500:	learn: 1.0592277	test: 1.0914758	best: 1.0914450 (13267)	total: 1m 55s	remaining: 5m 12s
14000:	learn: 1.0583366	test: 1.0915340	best: 1.0914450 (13267)	total: 1m 59s	remaining: 5m 7s
bestTest = 1.091444957
bestIteration = 13267
Shrink model to first 13268 iterations.
Fold 5/9
0:	learn: 1.3803314	test: 1.3803449	best: 1.3803449 (0)	total: 12.6ms	remaining: 10m 28s
500:	learn: 1.1014738	test: 1.1040353	best: 1.1040353 (500)	total: 4.28s	remaining: 7m 2s
1000:	learn: 1.0941530	test: 1.0986081	best: 1.0986081 (1000)	total: 8.37s	remaining: 6m 49s
1500:	learn: 1.0896659	test: 1.0959450	best: 1.0959450 (1500)	total: 12.5s	remaining: 6m 42s
2000:	learn: 1.0865247	test: 1.0945314	best: 1.0945314 (2000)	total: 16.6s	remaining: 6m 39s
2500:	learn: 1.0841118	test: 1.0936329	best: 1.0936329 (2500)	total: 20.9s	remaining: 6m 37s
3000:	learn: 1.0821140	test: 1.0931035	best: 1.0931033 (2996)	total: 25.1s	remaining: 6m 33s
3500:	learn: 1.0803786	test: 1.0926626	best: 1.0926626 (3500)	total: 29.4s	remaining: 6m 30s
4000:	learn: 1.0787835	test: 1.0923468	best: 1.0923461 (3998)	total: 33.6s	remaining: 6m 26s
4500:	learn: 1.0773503	test: 1.0921592	best: 1.0921584 (4499)	total: 37.8s	remaining: 6m 21s
5000:	learn: 1.0759937	test: 1.0919459	best: 1.0919427 (4996)	total: 41.9s	remaining: 6m 16s
5500:	learn: 1.0747340	test: 1.0917758	best: 1.0917746 (5497)	total: 46s	remaining: 6m 11s
6000:	learn: 1.0734842	test: 1.0916865	best: 1.0916852 (5999)	total: 50.2s	remaining: 6m 8s
6500:	learn: 1.0722982	test: 1.0916173	best: 1.0916163 (6497)	total: 54.3s	remaining: 6m 3s
7000:	learn: 1.0711769	test: 1.0915335	best: 1.0915318 (6984)	total: 58.5s	remaining: 5m 59s
7500:	learn: 1.0700728	test: 1.0914792	best: 1.0914791 (7499)	total: 1m 2s	remaining: 5m 54s
8000:	learn: 1.0690089	test: 1.0914509	best: 1.0914457 (7933)	total: 1m 6s	remaining: 5m 51s
8500:	learn: 1.0680155	test: 1.0913538	best: 1.0913506 (8489)	total: 1m 11s	remaining: 5m 48s
9000:	learn: 1.0670159	test: 1.0913280	best: 1.0913180 (8652)	total: 1m 15s	remaining: 5m 44s
9500:	learn: 1.0659491	test: 1.0913202	best: 1.0913132 (9153)	total: 1m 19s	remaining: 5m 40s
10000:	learn: 1.0649719	test: 1.0913347	best: 1.0913132 (9153)	total: 1m 24s	remaining: 5m 36s
bestTest = 1.091313208
bestIteration = 9153
Shrink model to first 9154 iterations.
Fold 6/9
0:	learn: 1.3803284	test: 1.3803183	best: 1.3803183 (0)	total: 13ms	remaining: 10m 51s
500:	learn: 1.1014565	test: 1.1042888	best: 1.1042888 (500)	total: 4.3s	remaining: 7m 5s
1000:	learn: 1.0939194	test: 1.0990268	best: 1.0990268 (1000)	total: 8.44s	remaining: 6m 53s
1500:	learn: 1.0895067	test: 1.0965232	best: 1.0965232 (1500)	total: 12.6s	remaining: 6m 48s
2000:	learn: 1.0864118	test: 1.0951678	best: 1.0951678 (2000)	total: 16.7s	remaining: 6m 41s
2500:	learn: 1.0840359	test: 1.0943758	best: 1.0943758 (2500)	total: 20.9s	remaining: 6m 36s
3000:	learn: 1.0821647	test: 1.0938254	best: 1.0938254 (3000)	total: 25s	remaining: 6m 31s
3500:	learn: 1.0804890	test: 1.0934345	best: 1.0934345 (3500)	total: 29.1s	remaining: 6m 27s
4000:	learn: 1.0789754	test: 1.0930913	best: 1.0930913 (4000)	total: 33.5s	remaining: 6m 24s
4500:	learn: 1.0775588	test: 1.0928409	best: 1.0928390 (4489)	total: 37.6s	remaining: 6m 20s
5000:	learn: 1.0762550	test: 1.0926491	best: 1.0926472 (4985)	total: 41.8s	remaining: 6m 16s
5500:	learn: 1.0750020	test: 1.0925274	best: 1.0925254 (5497)	total: 46.1s	remaining: 6m 13s
6000:	learn: 1.0738139	test: 1.0924101	best: 1.0924101 (6000)	total: 50.3s	remaining: 6m 8s
6500:	learn: 1.0726449	test: 1.0923371	best: 1.0923241 (6395)	total: 54.5s	remaining: 6m 4s
7000:	learn: 1.0715387	test: 1.0922752	best: 1.0922709 (6978)	total: 58.8s	remaining: 6m
7500:	learn: 1.0704833	test: 1.0922174	best: 1.0922144 (7472)	total: 1m 3s	remaining: 5m 57s
8000:	learn: 1.0694267	test: 1.0921637	best: 1.0921616 (7970)	total: 1m 7s	remaining: 5m 53s
8500:	learn: 1.0683635	test: 1.0921504	best: 1.0921348 (8237)	total: 1m 11s	remaining: 5m 49s
9000:	learn: 1.0673708	test: 1.0921086	best: 1.0920974 (8988)	total: 1m 15s	remaining: 5m 44s
9500:	learn: 1.0664052	test: 1.0921224	best: 1.0920914 (9221)	total: 1m 19s	remaining: 5m 40s
10000:	learn: 1.0653922	test: 1.0921083	best: 1.0920914 (9221)	total: 1m 24s	remaining: 5m 36s
bestTest = 1.092091399
bestIteration = 9221
Shrink model to first 9222 iterations.
Fold 7/9
0:	learn: 1.3803307	test: 1.3803195	best: 1.3803195 (0)	total: 12.7ms	remaining: 10m 33s
500:	learn: 1.1015442	test: 1.1027321	best: 1.1027321 (500)	total: 4.43s	remaining: 7m 18s
1000:	learn: 1.0940318	test: 1.0972145	best: 1.0972145 (1000)	total: 8.69s	remaining: 7m 5s
1500:	learn: 1.0894462	test: 1.0946710	best: 1.0946710 (1500)	total: 12.8s	remaining: 6m 54s
2000:	learn: 1.0862843	test: 1.0933526	best: 1.0933526 (2000)	total: 17.2s	remaining: 6m 51s
2500:	learn: 1.0839716	test: 1.0925633	best: 1.0925633 (2500)	total: 21.4s	remaining: 6m 46s
3000:	learn: 1.0820289	test: 1.0921293	best: 1.0921293 (3000)	total: 25.7s	remaining: 6m 41s
3500:	learn: 1.0803669	test: 1.0918110	best: 1.0918110 (3500)	total: 29.8s	remaining: 6m 35s
4000:	learn: 1.0787970	test: 1.0915091	best: 1.0915082 (3998)	total: 34.1s	remaining: 6m 32s
4500:	learn: 1.0774087	test: 1.0912924	best: 1.0912924 (4500)	total: 38.4s	remaining: 6m 28s
5000:	learn: 1.0760678	test: 1.0911549	best: 1.0911423 (4968)	total: 42.6s	remaining: 6m 23s
5500:	learn: 1.0747681	test: 1.0910451	best: 1.0910440 (5496)	total: 46.9s	remaining: 6m 18s
6000:	learn: 1.0735468	test: 1.0909167	best: 1.0909054 (5988)	total: 51s	remaining: 6m 13s
6500:	learn: 1.0723882	test: 1.0908264	best: 1.0908207 (6442)	total: 55.3s	remaining: 6m 9s
7000:	learn: 1.0712688	test: 1.0907837	best: 1.0907691 (6873)	total: 59.5s	remaining: 6m 5s
7500:	learn: 1.0701863	test: 1.0907851	best: 1.0907691 (6873)	total: 1m 3s	remaining: 6m
8000:	learn: 1.0691249	test: 1.0907363	best: 1.0907205 (7831)	total: 1m 7s	remaining: 5m 56s
8500:	learn: 1.0680626	test: 1.0907397	best: 1.0907205 (7831)	total: 1m 12s	remaining: 5m 51s
bestTest = 1.090720468
bestIteration = 7831
Shrink model to first 7832 iterations.
Fold 8/9
0:	learn: 1.3803364	test: 1.3803246	best: 1.3803246 (0)	total: 12.6ms	remaining: 10m 27s
500:	learn: 1.1016339	test: 1.1042319	best: 1.1042319 (500)	total: 4.34s	remaining: 7m 8s
1000:	learn: 1.0942059	test: 1.0989234	best: 1.0989234 (1000)	total: 8.69s	remaining: 7m 5s
1500:	learn: 1.0897719	test: 1.0963687	best: 1.0963687 (1500)	total: 13s	remaining: 7m 1s
2000:	learn: 1.0866743	test: 1.0949455	best: 1.0949455 (2000)	total: 17.3s	remaining: 6m 56s
2500:	learn: 1.0843412	test: 1.0940695	best: 1.0940695 (2500)	total: 21.8s	remaining: 6m 53s
3000:	learn: 1.0824210	test: 1.0934256	best: 1.0934256 (3000)	total: 26.1s	remaining: 6m 48s
3500:	learn: 1.0807248	test: 1.0929944	best: 1.0929944 (3500)	total: 30.3s	remaining: 6m 42s
4000:	learn: 1.0792230	test: 1.0926623	best: 1.0926622 (3999)	total: 34.5s	remaining: 6m 36s
4500:	learn: 1.0778197	test: 1.0923751	best: 1.0923747 (4498)	total: 38.9s	remaining: 6m 32s
5000:	learn: 1.0764769	test: 1.0921022	best: 1.0921020 (4994)	total: 43.2s	remaining: 6m 28s
5500:	learn: 1.0752310	test: 1.0919327	best: 1.0919327 (5500)	total: 47.5s	remaining: 6m 23s
6000:	learn: 1.0740676	test: 1.0917895	best: 1.0917893 (5999)	total: 51.7s	remaining: 6m 19s
6500:	learn: 1.0728541	test: 1.0916740	best: 1.0916713 (6496)	total: 56s	remaining: 6m 14s
7000:	learn: 1.0717546	test: 1.0915627	best: 1.0915625 (6997)	total: 1m	remaining: 6m 10s
7500:	learn: 1.0707187	test: 1.0914829	best: 1.0914786 (7475)	total: 1m 4s	remaining: 6m 6s
8000:	learn: 1.0697057	test: 1.0914314	best: 1.0914200 (7919)	total: 1m 8s	remaining: 6m 2s
8500:	learn: 1.0687066	test: 1.0914084	best: 1.0913974 (8470)	total: 1m 13s	remaining: 5m 57s
9000:	learn: 1.0677097	test: 1.0913371	best: 1.0913318 (8977)	total: 1m 17s	remaining: 5m 52s
9500:	learn: 1.0667317	test: 1.0912721	best: 1.0912713 (9499)	total: 1m 21s	remaining: 5m 48s
10000:	learn: 1.0657744	test: 1.0912382	best: 1.0912323 (9966)	total: 1m 25s	remaining: 5m 43s
10500:	learn: 1.0648119	test: 1.0912622	best: 1.0912323 (9966)	total: 1m 30s	remaining: 5m 39s
bestTest = 1.09123226
bestIteration = 9966
Shrink model to first 9967 iterations.

Picking model 3 which had the lowest loss

models[2]['model'].predict_proba(X_train)
submissionFile(models[3]['model'])
!kaggle competitions submit -c tabular-playground-series-may-2021 -f submission.csv -m "CatBoost-Classifier"

Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)
100% 4.06M/4.06M [00:01<00:00, 2.22MB/s]
Successfully submitted to Tabular Playground Series - May 2021

This model gave us the best performance. I will now try to improve the performance using some feature engineering and feature

cat_vars = []
num_vars = []
for i in indep_vars:
    unique = list(train[i].unique())
    unique.sort()
    if (unique[-1] == (len(unique) -1)):
        cat_vars.append(i)
    else:
        num_vars.append(i)

print(cat_vars)
print(num_vars)

['feature_0', 'feature_2', 'feature_5', 'feature_6', 'feature_7', 'feature_9', 'feature_10', 'feature_11', 'feature_12', 'feature_13', 'feature_14', 'feature_15', 'feature_16', 'feature_17', 'feature_18', 'feature_20', 'feature_22', 'feature_23', 'feature_24', 'feature_26', 'feature_27', 'feature_28', 'feature_29', 'feature_33', 'feature_34', 'feature_35', 'feature_36', 'feature_37', 'feature_40', 'feature_44', 'feature_45', 'feature_46', 'feature_47', 'feature_48', 'feature_49']
['feature_1', 'feature_3', 'feature_4', 'feature_8', 'feature_19', 'feature_21', 'feature_25', 'feature_30', 'feature_31', 'feature_32', 'feature_38', 'feature_39', 'feature_41', 'feature_42', 'feature_43']

Using t a proxy to see if the number of levels is equal to the number of unique elements in the list and using numberical varaible sfor the others.

models = train_models(df=train, columns=indep_vars, cat_features=cat_vars, random_state=43, n_splits=9)

Fold 0/9
0:	learn: 1.3803154	test: 1.3802907	best: 1.3802907 (0)	total: 16ms	remaining: 13m 21s
500:	learn: 1.1014573	test: 1.1042078	best: 1.1042078 (500)	total: 4.62s	remaining: 7m 36s
1000:	learn: 1.0941843	test: 1.0986681	best: 1.0986681 (1000)	total: 8.94s	remaining: 7m 17s
1500:	learn: 1.0897845	test: 1.0957679	best: 1.0957679 (1500)	total: 13.2s	remaining: 7m 5s
2000:	learn: 1.0866740	test: 1.0941196	best: 1.0941196 (2000)	total: 17.5s	remaining: 6m 59s
2500:	learn: 1.0842484	test: 1.0931565	best: 1.0931552 (2499)	total: 21.7s	remaining: 6m 51s
3000:	learn: 1.0821899	test: 1.0924521	best: 1.0924521 (3000)	total: 25.9s	remaining: 6m 45s
3500:	learn: 1.0803967	test: 1.0919472	best: 1.0919445 (3492)	total: 30s	remaining: 6m 39s
4000:	learn: 1.0787984	test: 1.0915882	best: 1.0915839 (3992)	total: 34.2s	remaining: 6m 33s
4500:	learn: 1.0773583	test: 1.0912775	best: 1.0912775 (4500)	total: 38.4s	remaining: 6m 28s
5000:	learn: 1.0759195	test: 1.0910710	best: 1.0910687 (4994)	total: 42.7s	remaining: 6m 24s
5500:	learn: 1.0745877	test: 1.0909247	best: 1.0909247 (5500)	total: 47.1s	remaining: 6m 20s
6000:	learn: 1.0733407	test: 1.0908060	best: 1.0907974 (5988)	total: 51.4s	remaining: 6m 16s
6500:	learn: 1.0721179	test: 1.0907056	best: 1.0907031 (6491)	total: 55.7s	remaining: 6m 12s
7000:	learn: 1.0709431	test: 1.0906437	best: 1.0906366 (6956)	total: 1m	remaining: 6m 8s
7500:	learn: 1.0697953	test: 1.0905883	best: 1.0905883 (7500)	total: 1m 4s	remaining: 6m 3s
8000:	learn: 1.0686584	test: 1.0905239	best: 1.0905210 (7991)	total: 1m 8s	remaining: 5m 59s
8500:	learn: 1.0675881	test: 1.0904979	best: 1.0904962 (8353)	total: 1m 12s	remaining: 5m 54s
9000:	learn: 1.0665645	test: 1.0904682	best: 1.0904536 (8909)	total: 1m 16s	remaining: 5m 50s
9500:	learn: 1.0654970	test: 1.0905020	best: 1.0904536 (8909)	total: 1m 21s	remaining: 5m 46s
bestTest = 1.090453574
bestIteration = 8909
Shrink model to first 8910 iterations.
Fold 1/9
0:	learn: 1.3803101	test: 1.3803033	best: 1.3803033 (0)	total: 12.8ms	remaining: 10m 41s
500:	learn: 1.1014775	test: 1.1023033	best: 1.1023033 (500)	total: 4.38s	remaining: 7m 12s
1000:	learn: 1.0940323	test: 1.0969530	best: 1.0969530 (1000)	total: 8.54s	remaining: 6m 57s
1500:	learn: 1.0895466	test: 1.0944405	best: 1.0944405 (1500)	total: 12.7s	remaining: 6m 51s
2000:	learn: 1.0863741	test: 1.0930538	best: 1.0930534 (1999)	total: 17s	remaining: 6m 46s
2500:	learn: 1.0839846	test: 1.0922012	best: 1.0922012 (2500)	total: 21.1s	remaining: 6m 41s
3000:	learn: 1.0819736	test: 1.0916348	best: 1.0916348 (3000)	total: 25.4s	remaining: 6m 38s
3500:	learn: 1.0802307	test: 1.0912422	best: 1.0912403 (3499)	total: 29.7s	remaining: 6m 34s
4000:	learn: 1.0786610	test: 1.0909507	best: 1.0909507 (4000)	total: 34.1s	remaining: 6m 31s
4500:	learn: 1.0772216	test: 1.0907130	best: 1.0907130 (4499)	total: 38.4s	remaining: 6m 28s
5000:	learn: 1.0758558	test: 1.0905153	best: 1.0905133 (4998)	total: 42.6s	remaining: 6m 23s
5500:	learn: 1.0745999	test: 1.0904333	best: 1.0904333 (5500)	total: 46.9s	remaining: 6m 19s
6000:	learn: 1.0733469	test: 1.0903090	best: 1.0903083 (5998)	total: 51.2s	remaining: 6m 15s
6500:	learn: 1.0721418	test: 1.0902650	best: 1.0902560 (6285)	total: 55.6s	remaining: 6m 11s
7000:	learn: 1.0709799	test: 1.0901836	best: 1.0901814 (6985)	total: 59.8s	remaining: 6m 7s
7500:	learn: 1.0698511	test: 1.0901672	best: 1.0901460 (7320)	total: 1m 4s	remaining: 6m 2s
8000:	learn: 1.0687475	test: 1.0901447	best: 1.0901229 (7917)	total: 1m 8s	remaining: 5m 58s
8500:	learn: 1.0676719	test: 1.0901045	best: 1.0901041 (8498)	total: 1m 12s	remaining: 5m 53s
9000:	learn: 1.0665936	test: 1.0900820	best: 1.0900766 (8822)	total: 1m 16s	remaining: 5m 49s
9500:	learn: 1.0656032	test: 1.0900599	best: 1.0900425 (9419)	total: 1m 21s	remaining: 5m 45s
10000:	learn: 1.0645814	test: 1.0900944	best: 1.0900425 (9419)	total: 1m 25s	remaining: 5m 41s
bestTest = 1.090042473
bestIteration = 9419
Shrink model to first 9420 iterations.
Fold 2/9
0:	learn: 1.3803122	test: 1.3802985	best: 1.3802985 (0)	total: 15.6ms	remaining: 12m 59s
500:	learn: 1.1024273	test: 1.1026459	best: 1.1026459 (500)	total: 4.53s	remaining: 7m 27s
1000:	learn: 1.0951133	test: 1.0967358	best: 1.0967358 (1000)	total: 8.79s	remaining: 7m 10s
1500:	learn: 1.0905104	test: 1.0937691	best: 1.0937691 (1500)	total: 13s	remaining: 7m 1s
2000:	learn: 1.0872891	test: 1.0920980	best: 1.0920980 (2000)	total: 17.3s	remaining: 6m 54s
2500:	learn: 1.0847069	test: 1.0910528	best: 1.0910528 (2500)	total: 21.5s	remaining: 6m 47s
3000:	learn: 1.0826028	test: 1.0902546	best: 1.0902546 (3000)	total: 25.8s	remaining: 6m 43s
3500:	learn: 1.0808443	test: 1.0897330	best: 1.0897330 (3500)	total: 29.9s	remaining: 6m 37s
4000:	learn: 1.0792220	test: 1.0893461	best: 1.0893436 (3998)	total: 34.2s	remaining: 6m 33s
4500:	learn: 1.0777600	test: 1.0890520	best: 1.0890489 (4497)	total: 38.5s	remaining: 6m 29s
5000:	learn: 1.0763837	test: 1.0887589	best: 1.0887548 (4994)	total: 42.9s	remaining: 6m 25s
5500:	learn: 1.0750925	test: 1.0885333	best: 1.0885327 (5498)	total: 47.1s	remaining: 6m 20s
6000:	learn: 1.0739088	test: 1.0883788	best: 1.0883680 (5971)	total: 51.3s	remaining: 6m 16s
6500:	learn: 1.0727298	test: 1.0882471	best: 1.0882369 (6442)	total: 55.6s	remaining: 6m 12s
7000:	learn: 1.0715518	test: 1.0881835	best: 1.0881835 (7000)	total: 59.9s	remaining: 6m 7s
7500:	learn: 1.0704446	test: 1.0880607	best: 1.0880607 (7500)	total: 1m 4s	remaining: 6m 3s
8000:	learn: 1.0693462	test: 1.0880266	best: 1.0880101 (7903)	total: 1m 8s	remaining: 5m 59s
8500:	learn: 1.0682645	test: 1.0879302	best: 1.0879299 (8498)	total: 1m 12s	remaining: 5m 55s
9000:	learn: 1.0671927	test: 1.0878781	best: 1.0878698 (8989)	total: 1m 17s	remaining: 5m 50s
9500:	learn: 1.0662005	test: 1.0878672	best: 1.0878547 (9208)	total: 1m 21s	remaining: 5m 45s
10000:	learn: 1.0651930	test: 1.0878426	best: 1.0878382 (9975)	total: 1m 25s	remaining: 5m 41s
10500:	learn: 1.0641668	test: 1.0878097	best: 1.0877999 (10373)	total: 1m 29s	remaining: 5m 37s
11000:	learn: 1.0631846	test: 1.0878117	best: 1.0877961 (10536)	total: 1m 33s	remaining: 5m 32s
11500:	learn: 1.0621849	test: 1.0877742	best: 1.0877612 (11377)	total: 1m 38s	remaining: 5m 28s
12000:	learn: 1.0612432	test: 1.0877743	best: 1.0877605 (11583)	total: 1m 42s	remaining: 5m 23s
12500:	learn: 1.0602935	test: 1.0876917	best: 1.0876908 (12493)	total: 1m 46s	remaining: 5m 18s
13000:	learn: 1.0593720	test: 1.0877184	best: 1.0876823 (12581)	total: 1m 50s	remaining: 5m 14s
13500:	learn: 1.0584008	test: 1.0877127	best: 1.0876823 (12581)	total: 1m 54s	remaining: 5m 10s
bestTest = 1.087682322
bestIteration = 12581
Shrink model to first 12582 iterations.
Fold 3/9
0:	learn: 1.3803127	test: 1.3803101	best: 1.3803101 (0)	total: 12.9ms	remaining: 10m 46s
500:	learn: 1.1017681	test: 1.1031726	best: 1.1031726 (500)	total: 4.36s	remaining: 7m 10s
1000:	learn: 1.0944333	test: 1.0974920	best: 1.0974920 (1000)	total: 8.63s	remaining: 7m 2s
1500:	learn: 1.0900347	test: 1.0946097	best: 1.0946097 (1500)	total: 12.9s	remaining: 6m 56s
2000:	learn: 1.0870051	test: 1.0929751	best: 1.0929751 (2000)	total: 17.1s	remaining: 6m 50s
2500:	learn: 1.0845992	test: 1.0919531	best: 1.0919531 (2497)	total: 21.3s	remaining: 6m 43s
3000:	learn: 1.0826002	test: 1.0912773	best: 1.0912773 (3000)	total: 25.4s	remaining: 6m 37s
3500:	learn: 1.0807738	test: 1.0907599	best: 1.0907597 (3496)	total: 29.5s	remaining: 6m 32s
4000:	learn: 1.0792628	test: 1.0904421	best: 1.0904421 (4000)	total: 33.7s	remaining: 6m 27s
4500:	learn: 1.0777962	test: 1.0900987	best: 1.0900966 (4499)	total: 38s	remaining: 6m 23s
5000:	learn: 1.0763488	test: 1.0898015	best: 1.0898002 (4997)	total: 42.2s	remaining: 6m 19s
5500:	learn: 1.0750432	test: 1.0895820	best: 1.0895813 (5495)	total: 46.4s	remaining: 6m 15s
6000:	learn: 1.0737583	test: 1.0894158	best: 1.0894158 (6000)	total: 50.7s	remaining: 6m 11s
6500:	learn: 1.0725823	test: 1.0892838	best: 1.0892801 (6470)	total: 54.9s	remaining: 6m 7s
7000:	learn: 1.0713857	test: 1.0891421	best: 1.0891385 (6996)	total: 59.2s	remaining: 6m 3s
7500:	learn: 1.0702679	test: 1.0891015	best: 1.0890997 (7491)	total: 1m 3s	remaining: 5m 59s
8000:	learn: 1.0691777	test: 1.0890040	best: 1.0890039 (7999)	total: 1m 7s	remaining: 5m 55s
8500:	learn: 1.0681099	test: 1.0889011	best: 1.0888992 (8474)	total: 1m 11s	remaining: 5m 50s
9000:	learn: 1.0670139	test: 1.0888386	best: 1.0888367 (8999)	total: 1m 16s	remaining: 5m 46s
9500:	learn: 1.0659383	test: 1.0887710	best: 1.0887683 (9497)	total: 1m 20s	remaining: 5m 42s
10000:	learn: 1.0648871	test: 1.0887145	best: 1.0887085 (9989)	total: 1m 24s	remaining: 5m 37s
10500:	learn: 1.0639034	test: 1.0886763	best: 1.0886763 (10500)	total: 1m 28s	remaining: 5m 33s
11000:	learn: 1.0629177	test: 1.0886715	best: 1.0886651 (10954)	total: 1m 32s	remaining: 5m 28s
11500:	learn: 1.0619223	test: 1.0886414	best: 1.0886380 (11484)	total: 1m 36s	remaining: 5m 24s
12000:	learn: 1.0609298	test: 1.0886194	best: 1.0886184 (11993)	total: 1m 41s	remaining: 5m 20s
12500:	learn: 1.0599296	test: 1.0886359	best: 1.0886067 (12113)	total: 1m 45s	remaining: 5m 15s
13000:	learn: 1.0589628	test: 1.0886468	best: 1.0886067 (12113)	total: 1m 49s	remaining: 5m 11s
bestTest = 1.088606677
bestIteration = 12113
Shrink model to first 12114 iterations.
Fold 4/9
0:	learn: 1.3803182	test: 1.3803176	best: 1.3803176 (0)	total: 16.8ms	remaining: 14m 1s
500:	learn: 1.1018065	test: 1.1042562	best: 1.1042562 (500)	total: 4.45s	remaining: 7m 19s
1000:	learn: 1.0944599	test: 1.0990125	best: 1.0990125 (1000)	total: 8.67s	remaining: 7m 4s
1500:	learn: 1.0899221	test: 1.0966293	best: 1.0966293 (1500)	total: 12.9s	remaining: 6m 56s
2000:	learn: 1.0867181	test: 1.0952475	best: 1.0952475 (2000)	total: 17.1s	remaining: 6m 51s
2500:	learn: 1.0841464	test: 1.0943245	best: 1.0943223 (2497)	total: 21.4s	remaining: 6m 47s
3000:	learn: 1.0820641	test: 1.0937153	best: 1.0937126 (2986)	total: 25.6s	remaining: 6m 41s
3500:	learn: 1.0802623	test: 1.0933357	best: 1.0933357 (3500)	total: 29.8s	remaining: 6m 35s
4000:	learn: 1.0786697	test: 1.0930864	best: 1.0930855 (3999)	total: 34.2s	remaining: 6m 32s
4500:	learn: 1.0771992	test: 1.0928401	best: 1.0928398 (4496)	total: 38.4s	remaining: 6m 27s
5000:	learn: 1.0758527	test: 1.0926493	best: 1.0926485 (4959)	total: 42.6s	remaining: 6m 23s
5500:	learn: 1.0745733	test: 1.0925654	best: 1.0925651 (5499)	total: 46.8s	remaining: 6m 18s
6000:	learn: 1.0733346	test: 1.0924467	best: 1.0924466 (5998)	total: 51s	remaining: 6m 14s
6500:	learn: 1.0721605	test: 1.0924068	best: 1.0924068 (6500)	total: 55.2s	remaining: 6m 9s
7000:	learn: 1.0709959	test: 1.0922489	best: 1.0922464 (6999)	total: 59.4s	remaining: 6m 4s
7500:	learn: 1.0698977	test: 1.0922305	best: 1.0922207 (7442)	total: 1m 3s	remaining: 6m
8000:	learn: 1.0688331	test: 1.0921705	best: 1.0921705 (8000)	total: 1m 7s	remaining: 5m 55s
8500:	learn: 1.0677496	test: 1.0921510	best: 1.0921370 (8255)	total: 1m 11s	remaining: 5m 51s
9000:	learn: 1.0666998	test: 1.0920911	best: 1.0920839 (8970)	total: 1m 16s	remaining: 5m 46s
9500:	learn: 1.0656690	test: 1.0920707	best: 1.0920604 (9489)	total: 1m 20s	remaining: 5m 42s
10000:	learn: 1.0646850	test: 1.0920616	best: 1.0920423 (9877)	total: 1m 24s	remaining: 5m 38s
10500:	learn: 1.0636748	test: 1.0920442	best: 1.0920399 (10491)	total: 1m 28s	remaining: 5m 34s
11000:	learn: 1.0626423	test: 1.0920652	best: 1.0920375 (10672)	total: 1m 33s	remaining: 5m 29s
11500:	learn: 1.0616836	test: 1.0920588	best: 1.0920375 (10672)	total: 1m 37s	remaining: 5m 25s
bestTest = 1.092037522
bestIteration = 10672
Shrink model to first 10673 iterations.
Fold 5/9
0:	learn: 1.3803104	test: 1.3803072	best: 1.3803072 (0)	total: 13.1ms	remaining: 10m 56s
500:	learn: 1.1014307	test: 1.1040594	best: 1.1040594 (500)	total: 4.35s	remaining: 7m 10s
1000:	learn: 1.0941142	test: 1.0986284	best: 1.0986284 (1000)	total: 8.58s	remaining: 7m
1500:	learn: 1.0897260	test: 1.0960901	best: 1.0960901 (1500)	total: 12.8s	remaining: 6m 54s
2000:	learn: 1.0864740	test: 1.0946577	best: 1.0946577 (2000)	total: 17.3s	remaining: 6m 54s
2500:	learn: 1.0839735	test: 1.0937056	best: 1.0937056 (2500)	total: 21.5s	remaining: 6m 48s
3000:	learn: 1.0819215	test: 1.0931310	best: 1.0931310 (3000)	total: 25.7s	remaining: 6m 41s
3500:	learn: 1.0801291	test: 1.0927177	best: 1.0927177 (3500)	total: 29.9s	remaining: 6m 36s
4000:	learn: 1.0785383	test: 1.0924249	best: 1.0924202 (3992)	total: 34.2s	remaining: 6m 32s
4500:	learn: 1.0770470	test: 1.0922091	best: 1.0922087 (4499)	total: 38.6s	remaining: 6m 30s
5000:	learn: 1.0756343	test: 1.0919956	best: 1.0919908 (4990)	total: 42.9s	remaining: 6m 26s
5500:	learn: 1.0743336	test: 1.0917720	best: 1.0917720 (5500)	total: 47.1s	remaining: 6m 21s
6000:	learn: 1.0730892	test: 1.0916953	best: 1.0916945 (5871)	total: 51.3s	remaining: 6m 16s
6500:	learn: 1.0718981	test: 1.0916123	best: 1.0916122 (6499)	total: 55.6s	remaining: 6m 12s
7000:	learn: 1.0707235	test: 1.0915373	best: 1.0915331 (6984)	total: 60s	remaining: 6m 8s
7500:	learn: 1.0695917	test: 1.0914868	best: 1.0914814 (7480)	total: 1m 4s	remaining: 6m 4s
8000:	learn: 1.0684860	test: 1.0914842	best: 1.0914726 (7816)	total: 1m 8s	remaining: 5m 59s
8500:	learn: 1.0674024	test: 1.0914167	best: 1.0914167 (8500)	total: 1m 12s	remaining: 5m 55s
9000:	learn: 1.0663457	test: 1.0914214	best: 1.0913910 (8559)	total: 1m 17s	remaining: 5m 51s
9500:	learn: 1.0652309	test: 1.0914581	best: 1.0913910 (8559)	total: 1m 21s	remaining: 5m 46s
bestTest = 1.091390992
bestIteration = 8559
Shrink model to first 8560 iterations.
Fold 6/9
0:	learn: 1.3803119	test: 1.3802077	best: 1.3802077 (0)	total: 12.8ms	remaining: 10m 40s
500:	learn: 1.1012177	test: 1.1042123	best: 1.1042123 (500)	total: 4.44s	remaining: 7m 19s
1000:	learn: 1.0937772	test: 1.0991498	best: 1.0991498 (1000)	total: 8.62s	remaining: 7m 2s
1500:	learn: 1.0893251	test: 1.0966669	best: 1.0966669 (1500)	total: 12.8s	remaining: 6m 53s
2000:	learn: 1.0861467	test: 1.0952870	best: 1.0952870 (2000)	total: 17s	remaining: 6m 47s
2500:	learn: 1.0837929	test: 1.0945202	best: 1.0945183 (2499)	total: 21.2s	remaining: 6m 42s
3000:	learn: 1.0818202	test: 1.0940128	best: 1.0940128 (3000)	total: 25.3s	remaining: 6m 36s
3500:	learn: 1.0800502	test: 1.0935884	best: 1.0935884 (3500)	total: 29.5s	remaining: 6m 32s
4000:	learn: 1.0784801	test: 1.0932164	best: 1.0932164 (4000)	total: 33.7s	remaining: 6m 27s
4500:	learn: 1.0770144	test: 1.0929658	best: 1.0929658 (4500)	total: 37.9s	remaining: 6m 22s
5000:	learn: 1.0756286	test: 1.0928349	best: 1.0928278 (4982)	total: 42s	remaining: 6m 18s
5500:	learn: 1.0743065	test: 1.0926796	best: 1.0926764 (5495)	total: 46.3s	remaining: 6m 14s
6000:	learn: 1.0730892	test: 1.0925430	best: 1.0925409 (5991)	total: 50.4s	remaining: 6m 9s
6500:	learn: 1.0718497	test: 1.0924557	best: 1.0924482 (6383)	total: 54.6s	remaining: 6m 5s
7000:	learn: 1.0706676	test: 1.0923787	best: 1.0923749 (6975)	total: 58.9s	remaining: 6m 1s
7500:	learn: 1.0695531	test: 1.0922924	best: 1.0922802 (7443)	total: 1m 3s	remaining: 5m 57s
8000:	learn: 1.0684475	test: 1.0922815	best: 1.0922802 (7443)	total: 1m 7s	remaining: 5m 53s
8500:	learn: 1.0673664	test: 1.0922768	best: 1.0922526 (8087)	total: 1m 11s	remaining: 5m 48s
9000:	learn: 1.0662907	test: 1.0923258	best: 1.0922526 (8087)	total: 1m 15s	remaining: 5m 44s
bestTest = 1.092252592
bestIteration = 8087
Shrink model to first 8088 iterations.
Fold 7/9
0:	learn: 1.3803138	test: 1.3803193	best: 1.3803193 (0)	total: 12.5ms	remaining: 10m 24s
500:	learn: 1.1014759	test: 1.1029298	best: 1.1029298 (500)	total: 4.4s	remaining: 7m 15s
1000:	learn: 1.0938746	test: 1.0975624	best: 1.0975624 (1000)	total: 8.64s	remaining: 7m 2s
1500:	learn: 1.0893628	test: 1.0951636	best: 1.0951636 (1500)	total: 12.9s	remaining: 6m 56s
2000:	learn: 1.0861765	test: 1.0938117	best: 1.0938117 (2000)	total: 17.2s	remaining: 6m 51s
2500:	learn: 1.0837931	test: 1.0930337	best: 1.0930336 (2499)	total: 21.4s	remaining: 6m 46s
3000:	learn: 1.0817789	test: 1.0925508	best: 1.0925508 (3000)	total: 25.7s	remaining: 6m 41s
3500:	learn: 1.0800546	test: 1.0921979	best: 1.0921976 (3499)	total: 30s	remaining: 6m 38s
4000:	learn: 1.0784680	test: 1.0919685	best: 1.0919685 (4000)	total: 34.2s	remaining: 6m 33s
4500:	learn: 1.0770228	test: 1.0917815	best: 1.0917815 (4500)	total: 38.4s	remaining: 6m 28s
5000:	learn: 1.0756528	test: 1.0915928	best: 1.0915919 (4998)	total: 42.7s	remaining: 6m 24s
5500:	learn: 1.0742996	test: 1.0914613	best: 1.0914559 (5480)	total: 46.9s	remaining: 6m 19s
6000:	learn: 1.0730244	test: 1.0913195	best: 1.0913184 (5997)	total: 51.1s	remaining: 6m 14s
6500:	learn: 1.0718051	test: 1.0913102	best: 1.0912872 (6363)	total: 55.5s	remaining: 6m 11s
7000:	learn: 1.0706347	test: 1.0912237	best: 1.0912160 (6949)	total: 59.8s	remaining: 6m 7s
7500:	learn: 1.0695225	test: 1.0911561	best: 1.0911549 (7496)	total: 1m 3s	remaining: 6m 2s
8000:	learn: 1.0684411	test: 1.0911268	best: 1.0911216 (7872)	total: 1m 8s	remaining: 5m 57s
8500:	learn: 1.0673589	test: 1.0911096	best: 1.0910944 (8237)	total: 1m 12s	remaining: 5m 52s
9000:	learn: 1.0663197	test: 1.0910999	best: 1.0910944 (8237)	total: 1m 16s	remaining: 5m 48s
9500:	learn: 1.0652775	test: 1.0910657	best: 1.0910646 (9481)	total: 1m 20s	remaining: 5m 43s
10000:	learn: 1.0642287	test: 1.0910393	best: 1.0910334 (9945)	total: 1m 24s	remaining: 5m 39s
10500:	learn: 1.0632407	test: 1.0910213	best: 1.0910088 (10301)	total: 1m 29s	remaining: 5m 35s
11000:	learn: 1.0622336	test: 1.0910073	best: 1.0910050 (10878)	total: 1m 33s	remaining: 5m 31s
11500:	learn: 1.0612726	test: 1.0909825	best: 1.0909825 (11500)	total: 1m 37s	remaining: 5m 27s
12000:	learn: 1.0603057	test: 1.0909591	best: 1.0909463 (11913)	total: 1m 41s	remaining: 5m 22s
12500:	learn: 1.0593497	test: 1.0909483	best: 1.0909463 (11913)	total: 1m 46s	remaining: 5m 18s
13000:	learn: 1.0583582	test: 1.0909672	best: 1.0909433 (12516)	total: 1m 50s	remaining: 5m 14s
13500:	learn: 1.0573900	test: 1.0909732	best: 1.0909433 (12516)	total: 1m 54s	remaining: 5m 9s
bestTest = 1.090943273
bestIteration = 12516
Shrink model to first 12517 iterations.
Fold 8/9
0:	learn: 1.3803127	test: 1.3803107	best: 1.3803107 (0)	total: 12.7ms	remaining: 10m 35s
500:	learn: 1.1014923	test: 1.1041659	best: 1.1041659 (500)	total: 4.31s	remaining: 7m 6s
1000:	learn: 1.0940984	test: 1.0990492	best: 1.0990492 (1000)	total: 8.55s	remaining: 6m 58s
1500:	learn: 1.0896841	test: 1.0966278	best: 1.0966278 (1500)	total: 12.6s	remaining: 6m 48s
2000:	learn: 1.0865593	test: 1.0952672	best: 1.0952672 (2000)	total: 16.7s	remaining: 6m 41s
2500:	learn: 1.0841247	test: 1.0943762	best: 1.0943740 (2498)	total: 20.8s	remaining: 6m 35s
3000:	learn: 1.0821537	test: 1.0937613	best: 1.0937613 (3000)	total: 25s	remaining: 6m 31s
3500:	learn: 1.0804087	test: 1.0933388	best: 1.0933388 (3498)	total: 29.2s	remaining: 6m 28s
4000:	learn: 1.0788011	test: 1.0929844	best: 1.0929844 (4000)	total: 33.5s	remaining: 6m 24s
4500:	learn: 1.0773483	test: 1.0927156	best: 1.0927156 (4500)	total: 37.8s	remaining: 6m 22s
5000:	learn: 1.0759686	test: 1.0924898	best: 1.0924854 (4995)	total: 42.1s	remaining: 6m 18s
5500:	learn: 1.0746448	test: 1.0923315	best: 1.0923315 (5500)	total: 46.3s	remaining: 6m 14s
6000:	learn: 1.0734219	test: 1.0921941	best: 1.0921894 (5967)	total: 50.5s	remaining: 6m 9s
6500:	learn: 1.0721951	test: 1.0921046	best: 1.0920994 (6475)	total: 54.6s	remaining: 6m 5s
7000:	learn: 1.0710155	test: 1.0919832	best: 1.0919817 (6997)	total: 58.9s	remaining: 6m 1s
7500:	learn: 1.0698942	test: 1.0919211	best: 1.0919088 (7478)	total: 1m 3s	remaining: 5m 58s
8000:	learn: 1.0688395	test: 1.0918913	best: 1.0918771 (7979)	total: 1m 7s	remaining: 5m 53s
8500:	learn: 1.0677724	test: 1.0918583	best: 1.0918500 (8465)	total: 1m 11s	remaining: 5m 49s
9000:	learn: 1.0667146	test: 1.0918117	best: 1.0918013 (8817)	total: 1m 15s	remaining: 5m 44s
9500:	learn: 1.0657222	test: 1.0917447	best: 1.0917447 (9500)	total: 1m 19s	remaining: 5m 40s
10000:	learn: 1.0647055	test: 1.0917024	best: 1.0916938 (9946)	total: 1m 24s	remaining: 5m 36s
10500:	learn: 1.0637241	test: 1.0917440	best: 1.0916938 (9946)	total: 1m 28s	remaining: 5m 32s
bestTest = 1.091693778
bestIteration = 9946
Shrink model to first 9947 iterations.

for model in models:
    print(model['model'].get_best_score())

{'learn': {'MultiClass': 1.0646599337087121}, 'validation': {'MultiClass': 1.090453574260934}}
{'learn': {'MultiClass': 1.0637241390948262}, 'validation': {'MultiClass': 1.090042472690352}}
{'learn': {'MultiClass': 1.0582702689590389}, 'validation': {'MultiClass': 1.0876823221357212}}
{'learn': {'MultiClass': 1.0587420652442934}, 'validation': {'MultiClass': 1.088606677082396}}
{'learn': {'MultiClass': 1.0613538197921002}, 'validation': {'MultiClass': 1.0920375219377194}}
{'learn': {'MultiClass': 1.065104195963505}, 'validation': {'MultiClass': 1.0913909920349203}}
{'learn': {'MultiClass': 1.066106675179156}, 'validation': {'MultiClass': 1.0922525924477995}}
{'learn': {'MultiClass': 1.0573636782954021}, 'validation': {'MultiClass': 1.0909432727139772}}
{'learn': {'MultiClass': 1.062833280833399}, 'validation': {'MultiClass': 1.0916937782659077}}

grid = {'learning_rate': [0.03, 0.01, 0.001, 0.1],
        'depth': [4, 6, 10],
        'max_ctr_complexity': [10, 15, 30]}


for model in models:
    print(model['model'].get_best_score())
    grid_search_result = model['model'].grid_search(grid, 
                                       X=train, 
                                       y=train_labels, 
                                       plot=True)

X_train = train[indep_vars]
y_train = train['target']
#for n_fold, (train_idx, valid_idx) in enumerate(folds.split(X_train,  y_train)):
#    print('Fold {}/{}'.format(n_fold, n_splits))
dataset = Pool(X_train, y_train, indep_vars)
model_2 = CatBoostClassifier(
    task_type="GPU",
    iterations=15000,
    od_wait=1000, od_type='Iter',
    min_data_in_leaf=1,
    loss_function='MultiClass'

)
grid = {'learning_rate': [0.03, 0.01, 0.001, 0.1],
'depth': [4, 6, 10],
'max_ctr_complexity': [10, 15, 30]}

grid_search_result = model_2.grid_search(grid, dataset, cv=10,
                                plot=True)

Streaming output truncated to the last 5000 lines.
13938:	learn: 1.0766900	test: 1.0987451	best: 1.0987451 (13937)	total: 4m 4s	remaining: 18.6s
13939:	learn: 1.0766886	test: 1.0987441	best: 1.0987441 (13939)	total: 4m 4s	remaining: 18.6s
13940:	learn: 1.0766870	test: 1.0987442	best: 1.0987441 (13939)	total: 4m 4s	remaining: 18.6s
13941:	learn: 1.0766857	test: 1.0987438	best: 1.0987438 (13941)	total: 4m 4s	remaining: 18.5s
13942:	learn: 1.0766839	test: 1.0987435	best: 1.0987435 (13942)	total: 4m 4s	remaining: 18.5s
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13959:	learn: 1.0766620	test: 1.0987396	best: 1.0987396 (13959)	total: 4m 4s	remaining: 18.2s
13960:	learn: 1.0766612	test: 1.0987393	best: 1.0987393 (13960)	total: 4m 4s	remaining: 18.2s
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13964:	learn: 1.0766554	test: 1.0987378	best: 1.0987378 (13964)	total: 4m 4s	remaining: 18.1s
13965:	learn: 1.0766536	test: 1.0987376	best: 1.0987376 (13965)	total: 4m 4s	remaining: 18.1s
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13968:	learn: 1.0766498	test: 1.0987369	best: 1.0987369 (13968)	total: 4m 4s	remaining: 18.1s
13969:	learn: 1.0766488	test: 1.0987369	best: 1.0987369 (13968)	total: 4m 4s	remaining: 18.1s
13970:	learn: 1.0766482	test: 1.0987368	best: 1.0987368 (13970)	total: 4m 4s	remaining: 18s
13971:	learn: 1.0766471	test: 1.0987363	best: 1.0987363 (13971)	total: 4m 4s	remaining: 18s
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13973:	learn: 1.0766446	test: 1.0987356	best: 1.0987356 (13973)	total: 4m 4s	remaining: 18s
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13975:	learn: 1.0766422	test: 1.0987351	best: 1.0987351 (13974)	total: 4m 4s	remaining: 17.9s
13976:	learn: 1.0766411	test: 1.0987348	best: 1.0987348 (13976)	total: 4m 4s	remaining: 17.9s
13977:	learn: 1.0766392	test: 1.0987349	best: 1.0987348 (13976)	total: 4m 5s	remaining: 17.9s
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13987:	learn: 1.0766265	test: 1.0987321	best: 1.0987321 (13987)	total: 4m 5s	remaining: 17.7s
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13990:	learn: 1.0766227	test: 1.0987312	best: 1.0987312 (13990)	total: 4m 5s	remaining: 17.7s
13991:	learn: 1.0766218	test: 1.0987309	best: 1.0987309 (13991)	total: 4m 5s	remaining: 17.7s
13992:	learn: 1.0766205	test: 1.0987304	best: 1.0987304 (13992)	total: 4m 5s	remaining: 17.6s
13993:	learn: 1.0766190	test: 1.0987305	best: 1.0987304 (13992)	total: 4m 5s	remaining: 17.6s
13994:	learn: 1.0766172	test: 1.0987304	best: 1.0987304 (13992)	total: 4m 5s	remaining: 17.6s
13995:	learn: 1.0766163	test: 1.0987299	best: 1.0987299 (13995)	total: 4m 5s	remaining: 17.6s
13996:	learn: 1.0766146	test: 1.0987296	best: 1.0987296 (13996)	total: 4m 5s	remaining: 17.6s
13997:	learn: 1.0766136	test: 1.0987292	best: 1.0987292 (13997)	total: 4m 5s	remaining: 17.6s
13998:	learn: 1.0766128	test: 1.0987286	best: 1.0987286 (13998)	total: 4m 5s	remaining: 17.5s
13999:	learn: 1.0766102	test: 1.0987281	best: 1.0987281 (13999)	total: 4m 5s	remaining: 17.5s
14000:	learn: 1.0766093	test: 1.0987282	best: 1.0987281 (13999)	total: 4m 5s	remaining: 17.5s
14001:	learn: 1.0766076	test: 1.0987274	best: 1.0987274 (14001)	total: 4m 5s	remaining: 17.5s
14002:	learn: 1.0766065	test: 1.0987272	best: 1.0987272 (14002)	total: 4m 5s	remaining: 17.5s
14003:	learn: 1.0766051	test: 1.0987271	best: 1.0987271 (14003)	total: 4m 5s	remaining: 17.5s
14004:	learn: 1.0766042	test: 1.0987265	best: 1.0987265 (14004)	total: 4m 5s	remaining: 17.4s
14005:	learn: 1.0766025	test: 1.0987264	best: 1.0987264 (14005)	total: 4m 5s	remaining: 17.4s
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14008:	learn: 1.0765993	test: 1.0987254	best: 1.0987254 (14008)	total: 4m 5s	remaining: 17.4s
14009:	learn: 1.0765982	test: 1.0987257	best: 1.0987254 (14008)	total: 4m 5s	remaining: 17.4s
14010:	learn: 1.0765972	test: 1.0987252	best: 1.0987252 (14010)	total: 4m 5s	remaining: 17.3s
14011:	learn: 1.0765959	test: 1.0987243	best: 1.0987243 (14011)	total: 4m 5s	remaining: 17.3s
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bestTest = 1.098465332
bestIteration = 14999
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bestTest = 1.097422168
bestIteration = 446
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bestTest = 1.09834209
bestIteration = 653
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824:	learn: 1.0396452	test: 1.0998932	best: 1.0998932 (824)	total: 54.9s	remaining: 15m 42s
825:	learn: 1.0396149	test: 1.0998924	best: 1.0998924 (825)	total: 54.9s	remaining: 15m 42s
826:	learn: 1.0395838	test: 1.0998844	best: 1.0998844 (826)	total: 55s	remaining: 15m 41s
827:	learn: 1.0394804	test: 1.0998854	best: 1.0998844 (826)	total: 55s	remaining: 15m 41s

models[3]['model'].predict_proba(X_train)
submissionFile(models[3]['model'])
!kaggle competitions submit -c tabular-playground-series-may-2021 -f submission.csv -m "CatBoost-Classifier-CatvsNum"

Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)
100% 4.06M/4.06M [00:02<00:00, 1.54MB/s]
Successfully submitted to Tabular Playground Series - May 2021

Look at scaling the non categorical features in this to.

train2 = train.copy()
train2[num_vars].describe()

	feature_1	feature_3	feature_4	feature_8	feature_19	feature_21	feature_25	feature_30	feature_31	feature_32	feature_38	feature_39	feature_41	feature_42	feature_43
count	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000	100000.000000
mean	0.431720	0.590550	0.599770	0.903350	1.564780	0.661970	0.408630	0.294500	1.372040	0.277510	2.997170	0.674640	0.582070	0.529230	0.616310
std	1.977862	1.844558	2.785531	3.415258	4.749449	2.182703	1.321669	1.352963	3.319325	1.460705	5.386551	3.732428	2.003114	2.300826	2.360955
min	0.000000	0.000000	0.000000	0.000000	-2.000000	0.000000	0.000000	-1.000000	-1.000000	-2.000000	-8.000000	-5.000000	0.000000	-2.000000	0.000000
25%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000
75%	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	1.000000	0.000000	4.000000	0.000000	0.000000	0.000000	0.000000
max	31.000000	26.000000	38.000000	39.000000	55.000000	36.000000	23.000000	41.000000	46.000000	27.000000	66.000000	66.000000	32.000000	37.000000	33.000000

for i in num_vars:
    a = list(train2[i].unique())
    a.sort()
    print(a)

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 39]
[-2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23]
[-1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41]
[-1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 46]
[-2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]
[-8, -5, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 65, 66]
[-5, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 32]
[-2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33]

Maybe just choosing the negative numbers as numeric will help imporve the predictions

Had some other professional commitments come up and couldn't work in this as much as ai would have liked. Will try to do better in the June Competerion

Appendix

Here some some attempts and model modifications that did not yeild any improvements:

Principal Component Analysis

While going throu

from sklearn.decomposition import PCA
pca = PCA()
pca.fit(X)
print(pca.explained_variance_ratio_)

import seaborn as sns
sns.barplot(x=[i for i in range(50)], y=pca.explained_variance_ratio_)

vars = list(pca.explained_variance_ratio_)
sum(vars[:35])

So 95% of the variance is explained by 35 factors. We can run PCA again to reduce the dimensionality to 35 from 50

pca_35 = PCA(n_components=35)
X_35 = pca_35.fit_transform(X)

UMAP (Universal Manifold Approximation and Projection)

UMAP is a dimesion reduction method that can be used to visualize data in multi dimensions. It is based on the following assumptions.

- Data is uniformly distributed on Reimannian manifold

-

n= 4 to see if we can map directly to classes

!pip install umap-learn
import umap

Requirement already satisfied: umap-learn in /usr/local/lib/python3.7/dist-packages (0.5.1)
Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (0.22.2.post1)
Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (1.19.5)
Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (1.4.1)
Requirement already satisfied: pynndescent>=0.5 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (0.5.2)
Requirement already satisfied: numba>=0.49 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (0.51.2)
Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22->umap-learn) (1.0.1)
Requirement already satisfied: llvmlite>=0.30 in /usr/local/lib/python3.7/dist-packages (from pynndescent>=0.5->umap-learn) (0.34.0)
Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba>=0.49->umap-learn) (56.1.0)

umapD = umap.UMAP(n_neighbors=10 ,n_components=35)
train_4d = umapD.fit_transform(X)
test_4d = umapD.fit_transform(X_test)

X_train, X_valid, y_train, y_valid = train_test_split(train_4d, y, test_size=0.3, random_state=42)

from sklearn.ensemble import RandomForestClassifier
rf_umap = RandomForestClassifier(n_jobs=-1, n_estimators=40, max_samples=70000,
       max_features=0.5, min_samples_leaf=5, oob_score=True)

rf_umap.fit(X_train, y_train)
print(rf_umap.score(X_train, y_train))
print(rf_umap.score(X_valid, y_valid))

0.7767428571428572
0.5609333333333333

from sklearn.metrics import plot_confusion_matrix
plot_confusion_matrix(rf_umap, X_valid, y_valid)

<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f4e849bf050>

def submissionFile(modelZ, generate_csv=True):  # 4d 
    probabilities = pd.DataFrame(modelZ.predict_proba(test_4d), columns=['Class_1','Class_2','Class_3','Class_4'])
    mySubmission = pd.DataFrame()
    mySubmission['id'] = test['id']
    mySubmission['Class_1'] = probabilities['Class_1']
    mySubmission['Class_2'] = probabilities['Class_2']
    mySubmission['Class_3'] = probabilities['Class_3']
    mySubmission['Class_4'] = probabilities['Class_4']
    if generate_csv:
        mySubmission.to_csv('submission.csv', index=False)
    return mySubmission

submissionFile(rf_umap, True)
!kaggle competitions submit -c tabular-playground-series-may-2021 -f submission.csv -m "Test-RF-Classifier"

Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)
100% 4.06M/4.06M [00:02<00:00, 2.01MB/s]
Successfully submitted to Tabular Playground Series - May 2021

Oversampling

One thing

!pip install -U imbalanced-learn

from imblearn.over_sampling import RandomOverSampler
ros = RandomOverSampler(random_state=0)
X_resampled, y_resampled = ros.fit_resample(X, y)

X_train, X_valid, y_train, y_valid = train_test_split(X_resampled, y_resampled, test_size=0.3, random_state=42)

rf2 = RandomForestClassifier(n_jobs=-1, n_estimators=40, max_samples=70000,
       max_features=0.5, min_samples_leaf=5, oob_score=True)

rf2.fit(X_train, y_train)

rf2.score(X_train, y_train)
rf2.score(X_valid, y_valid)

from sklearn.metrics import plot_confusion_matrix
plot_confusion_matrix(rf2, X_valid, y_valid)

model.predict_proba(X_test)

submission2 = submissionFile(rf2, generate_csv=True)

!kaggle competitions submit -c tabular-playground-series-may-2021 -f submission.csv -m "Random Oversampling - Randomforests"

Unfortunately did much more poorly on the test set. Most likely overfit here:

Undersampling

import numpy as np
from imblearn.under_sampling import RandomUnderSampler

rus = RandomUnderSampler(random_state=0, replacement=True)
X_resampled, y_resampled = rus.fit_resample(X, y)

X_train, X_valid, y_train, y_valid = train_test_split(X_resampled, y_resampled, test_size=0.3, random_state=42)

rf_rus = RandomForestClassifier(n_jobs=-1, n_estimators=40, max_samples=20000,
       max_features=0.5, min_samples_leaf=5, oob_score=True)

rf_rus.fit(X_train, y_train)
print(rf_rus.score(X_train, y_train))
rf_rus.score(X_valid, y_valid)

Definitely Overfit here, the score in training is over 90 but less than half that in the validation set.

from sklearn.metrics import plot_confusion_matrix
plot_confusion_matrix(rf_rus, X_valid, y_valid)

submission2 = submissionFile(rf_rus, generate_csv=True)

Bagging Classifier

from imblearn.ensemble import EasyEnsembleClassifier
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.3, random_state=42)

brf = EasyEnsembleClassifier(random_state=42)
brf.fit(X_train, y_train)

print(brf.score(X_train, y_train))
brf.score(X_valid, y_valid)

from sklearn.metrics import plot_confusion_matrix
plot_confusion_matrix(brf, X_valid, y_valid)

Combination of Over and Undersampling

from imblearn.combine import SMOTEENN
smote_enn = SMOTEENN(random_state=0)
X_resampled, y_resampled = smote_enn.fit_resample(X, y)

X_train, X_valid, y_train, y_valid = train_test_split(X_resampled, y_resampled, test_size=0.3, random_state=42)

rf_smoteEEN = RandomForestClassifier(n_jobs=-1, n_estimators=40, max_samples=70000,
       max_features=0.5, min_samples_leaf=5, oob_score=True)

rf_smoteEEN.fit(X_train, y_train)
print(rf_smoteEEN.score(X_train, y_train))
rf_smoteEEN.score(X_valid, y_valid)

/usr/local/lib/python3.7/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).
  "(https://pypi.org/project/six/).", FutureWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.
  warnings.warn(message, FutureWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
  warnings.warn(msg, category=FutureWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
  warnings.warn(msg, category=FutureWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
  warnings.warn(msg, category=FutureWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
  warnings.warn(msg, category=FutureWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
  warnings.warn(msg, category=FutureWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
  warnings.warn(msg, category=FutureWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
  warnings.warn(msg, category=FutureWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
  warnings.warn(msg, category=FutureWarning)

0.9208717740073235

0.7399358563181526

from sklearn.metrics import plot_confusion_matrix
plot_confusion_matrix(rf_smoteEEN, X_valid, y_valid)

Baseline model - SVM

To tackle the sparseness part, I will take a look at using Support Vector Machines.

train_sub = train.sample(10000, random_state=2)

proj_4d

from umap import UMAP

train_sub = train.sample(10000, random_state=0)
target = train_sub['target2']
umap = UMAP(random_state=0)
dr = umap.fit_transform(train_sub[indep_vars].iloc[:,:-1], target)

import plotly.express as px
fig_2d = px.scatter(
    dr, x=0, y=1, 
    labels={'color': 'target'},
    color=train_sub.target,
    color_discrete_sequence=px.colors.qualitative.G10,
)

fig_2d.show()

import plotly.express as px
fig_2d = px.scatter(
    proj_4d, x=0, y=1, 
    labels={'color': 'target'},
    color=train[''],
    color_discrete_sequence=px.colors.qualitative.G10,
)

fig_2d.show()

mapper = umap.UMAP(metric='cosine', random_state=42, low_memory=True).fit_transform(train_sub[indep_vars])
fig_mapper = px.scatter(
    mapper, x=0, y=1, 
    labels={'color': 'target'},
    color=train_sub.target,
    color_discrete_sequence=px.colors.qualitative.G10,
)

fig_mapper.show()

from sklearn import svm
clf = svm.SVC()

X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.3, random_state=42)

clf.fit(X_train, y_train)
clf.score(X_valid, y_valid)

Using the top important features only

imps = pd.DataFrame({'cols':indep_vars, 'imp':model.feature_importances_}).sort_values('imp', ascending=False)
imps[:10]

def plot_fi(fi):
    return fi.plot('cols', 'imp', 'barh', figsize=(12,7), legend=False)

plot_fi(imps[:30])

imp_features = imps[imps.imp>0.015].cols
len(imp_features)

X2 = (train[imp_features].to_numpy())
X2_test = test[imp_features].to_numpy()

model2 = rf(X2,y)
model2.predict_proba(X2_test)

Generate the submission

def submissionFile(modelZ, generate_csv=True):
    probabilities = pd.DataFrame(modelZ.predict_proba(X_test), columns=['Class_1','Class_2','Class_3','Class_4'])
    mySubmission = pd.DataFrame()
    mySubmission['id'] = test['id']
    mySubmission['Class_1'] = probabilities['Class_1']
    mySubmission['Class_2'] = probabilities['Class_2']
    mySubmission['Class_3'] = probabilities['Class_3']
    mySubmission['Class_4'] = probabilities['Class_4']
    if generate_csv:
        mySubmission.to_csv('submission.csv', index=False)
    return mySubmission

#!kaggle competitions submit -c tabular-playground-series-may-2021 -f submission.csv -m "Test-RF-Classifier"